ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionIdentifying disease-modifying drug targets is crucial for developing effective Alzheimer's disease (AD) treatments.
methodsWe present a deep contrastive learning framework for cell type-specific AD-associated genes identification (alzCL). alzCL creates cell-type-specific representations of genes by integrating human brain single-nucleus RNA-sequencing data with the human protein-protein interactome, thereby capturing both genetic signatures and functional features.
resultsBy integrating human brain snRNA-seq data, alzCL outperforms the state-of-the-art models by 18% to 24% in area under the receiver operating characteristic curve. Via alzCL, we computationally identified 16, 164, and 221 AD-associated genes across astrocytes, microglia, and inhibitory neurons, respectively. Top prioritized genes (e.g., DISCUSSION: AlzCL offers a deep contrastive learning framework for discovery of disease-associated genes and drug targets in AD.
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